Application of Condition Assessment Methodologies for Transformers

Presented By:
Brian Sparling
Dynamic Ratings
Chris Beckett
United Energy Australia
Tara-lee MacArthur
Ergon Energy Australia
TechCon 2020


Power transformers are expensive and critical equipment in power systems and play a significant role in the transmission and distribution of electricity. Although transformers are generally reliable pieces of equipment, failures do occur, as many degradation mechanisms are operating that affect components and sub-systems that will ultimately limit the useful operating life.

Transformer users and asset managers need to be adequately equipped to assess the condition of a fleet of transformers in service in their utility/organization as a basis for making critical decisions about operations. In other words, classifying candidates and priorities for repair/rectification of minor failures, refurbishment or replacement, and availability of reliable spare units when they may be required. Users and asset managers need to understand all the failure modes of transformers to pinpoint the part of the transformer affected and to identify appropriate responses. Broadly, there may be failures of inactive parts of transformers or their accessories due to dielectric, mechanical, or thermal breakdown. Some sub-components also have their own unique failure mechanisms.

This presentation and paper aim to focus on the work completed by the CIGRE WG A2.49 and will set out three unique case histories that apply the intent of the now-published Technical Brochure TB 761 Guide for Transformer Condition Assessment. The failure modes, the tests and diagnostic methods, and methods of combining the available data into useful information in the form of assessment indices that form the basis for decision-making and intervention prioritization in transformer asset management will be presented…

Key Words: Condition Assessment, Building Indices, Classifying Candidates.


The focus of Cigre Working Group A2.49 was to investigate information used to derive Transformer Assessment Indices (TAI), its consolidation, and the uses to which the output could be utilized. This paper gives three example TAIs that were developed to help identify transformers that needed repair, refurbishment or replacement.


TAIs can be generated by calculating a score for each transformer in the fleet, then using the assigned scores to rank the transformers. The five basic steps to develop a TAI are listed below, but the complete guide can be found in the Technical Brochure 761 Condition Assessment of Power Transformers, published in March 2019.

Step 1: Determine the purpose of the Transformer Assessment Score and Index

Many asset managers currently use a health index for prioritizing asset replacement. However, in many cases, the index does not provide any indication of how quickly the worst transformers on the list need to be actioned, nor does it give any indication of the most appropriate action needed i.e. replace, repair, or refurbish. This paper shows three examples of different uses of a TAI and some lessons learned by utilities in creating an assessment score and index.

Types of Transformer Assessment Indices

Reliability / Health Index is used to rank transformers based on the likelihood of failure or expected time to failure, and those that have degraded from their original condition. This index ranks all transformers that require any intervention, based on a sense of technical urgency.

Refurbishment Index is used to rank transformers based on their need and/or technical feasibility for refurbishment. This index should focus on multiple reversible failure modes or those failure modes that may involve excessive cost interventions and lifetime extension.

Repair Index is used to rank transformers that would most benefit from a repair or non-essential maintenance. The scoring is based on reversible failure modes, which can be corrected by repair or by performing maintenance.

Composite Index is an approach that combines the outcomes of the replacement and refurbishment or repair requirements. It should include the technical assessment and generic financial considerations, following the company guidelines for such financial justifications. The financial discussion and considerations are beyond the scope of TB 761.

Mitigation Index/Score Whilst typical indices assess the state of an asset, the approach can be adapted to evaluate the technical feasibility of a refurbishment or repair. When a repair or refurbishment score RSOriginal indicates that a transformer should be considered, the user can then calculate RSMitigated by using the same index and scoring system to calculate a score based on the assumed condition of the unit, after the intervention has been completed. The difference can then be one of the items considered to make the decision.

Step 2 and Step 3: Identify the failure modes and determine how each failure mode will be assessed in the TAI.

A clear understanding of the failure modes and interpretation of the results is necessary to ensure a reasonable correlation between the asset’s condition and the appropriate actions taken. The Technical Brochure includes a comprehensive guide to key transformer components, failure modes, and suitable condition assessment techniques that could be included in an assessment index. Examples of some of the diagnostic testing and failure modes that can be included in a TAI are shown in Figure 1.

Fig1 TAI comprised of diagnostic testing and an understanding of the different types of failure modes

Step 4: Design a calibrated system for categorizing failure modes (scoring matrix).

An example of a scoring matrix has been developed by the working group as detailed in Table 1. This matrix effectively has five levels. The 6th level labeled F is not used when generating a TAI but is noted to consider very short-term failure criteria.

Table1 – Example of a Scoring Matrix

Design (and test) a calibrated system for categorizing failure modes (scoring matrix) to obtain “Standard” results across the fleet from your data, as suggested in Table 1.

Different data require different interpretations. For example, paper condition:

  • DP is “end of life” at 200 and “Mid-life” at 400
  • Moisture below 1% wt./wt. is “business as usual,” but 5% is “Panic mode.”
  • CO at 1000 ppm is “to be noted,” but 10 000 ppm is “Definitely wrong.”

Step 5: Calculate a TAI Score for each transformer

There are multiple ways to generate an overall score. The method chosen will depend on the purpose of the TAI (Step 1) and the individual user’s needs.

When designing the scoring system, the following points should be considered:

  • The scoring system should allow all transformers in a fleet to be ranked, such that those which are the highest priority for action or intervention are easily identified.
  • The scoring system result should be easily interpretable by any user, with reference to the purpose of the TAI.

The scoring system should be transparent and reproducible.

Methods of Calculating a TAI Score as detailed in TB 761 with advantages and disadvantages noted for each include:

  1. Summation of individual failure mode scores;
  2. Weighted average;
  3. Non-linear mathematical approach;
  4. Numerical score using probabilities of failure;
  5. Worst-case approach;
  6. Hybrid score;
  7. Count per category;
  8. Machine learning.

Applications of TB 761 to date;

We have three examples of the concept included for discussion.

TAI 1 – Post-fleet screening

A utility had conducted a fleet screening of transformer assets to identify assets in poor condition. A total of 4 transformers were selected for further detailed assessment. A worst-case approach for the scoring index was used with numerous condition aspects analyzed for each transformer element. The worst score was used to form the component score, using a scoring system from A to F. A summary is shown below in Table 2.

Table2 – Detailed condition assessment results

Overall, there is not a lot separating the units as many of the transformer elements are in poor condition and some in reasonable condition. But what does that mean? Not a lot on its own, however, it starts the journey to determine the question to be answered.

What is the course of action for these units, if anything at all? To answer this question, an index is constructed, but there are many ways to construct an index. Each score must be assigned a weight.

Table 3 – Possible weighting factors

Weighting factors are useful to highlight transformer condition elements that are in poor condition and essential to prevent poor condition elements from being ‘hidden’ within the overall score. The non-linear method (#3 above), whilst relatively more complicated to generate, prevents masking completely by ensuring high scores are only possible with poor condition assessments – a score above 6561 is only possible if an E is present.

In order to consolidate into a comparable ranking, condition assessment scores were generated by applying the weighting systems in Table 3 to the available condition assessment data in Table 4. The various outputs are demonstrated in Table 4 below.

Table 4 – Index Scores

Now the results are starting to demonstrate something useful. Combining the detailed condition assessment with the weighting factors has resulted in distilling the assessment results into a single ranking. Interestingly, Unit 3 may appear to be in the worst condition (by account of the highest score) when using the simplest method, but by attributing higher weightings or using a non-linear approach to prevent hiding of components in poor condition, it becomes more evident that other units assessed have elements that are in worse condition.

But what should the user or asset manager do next? All the scores are similar (which should be no surprise – the fleet screening identified units that are in poor condition) – but different.

Should they all be replaced? What about refurbishment? Should anything be done at all?

The index does not clearly give the answer unless the index is designed with the question in mind in the first place. It may not be prudent to simply replace all transformers but a selection. Some transformers may be better addressed by a major refurbishment at a lower cost, delivering a better return on expenditure (by way of health score reduction) per dollar spent.

Further information on what actions are prudent for consideration can be generated by creating a specific index for the activity such as refurbishment or replacement by only including elements that are typically treatable for that activity. For example, active part elements generally are not assessed in a refurbishment index as the activity would do little to address that condition score. The approach was applied to the transformers under analysis and a sub-index was created for each one (with five transformer elements selected to construct a replacement score, and six elements selected for a refurbishment score). The scores are shown below for the geometric weighted approach:

Table 5 – Index Scores

From the table above, the replacement and refurbishment scores will differ from the overall score, hence the importance of knowing what to include when making a refurbishment and replacement ranking. Whilst all units exhibit similar overall scores, there are two clear front-runners for deteriorated conditions of non-repairable elements are Units 1 and 2, whilst Unit 4 is the more promising candidate for an effective refurbishment.

The decision of what to do, repair or replace is typically an economic decision and is outside the role of the condition assessment alone. It simply enables the prioritization for further analysis, such as consideration for the consequence of failure.

For an Asset Manager looking to prioritize the replacement of two transformers, Units 1 and 2 would be the preferred candidates, whilst Unit 4, followed by Unit 3, would be preferred for refurbishment. However, the substation risk when also considered in combination with the index may not be sufficient; if Unit 4 is located in a low-risk area with investment not warranted at the time of assessment, then Unit 3 could be refurbished first and further assessment of other transformers not initially selected would be required to find other candidates.

TAI 2 – Comparing two transformers operating in parallel

A transmission and distribution utility with a population of approximately 250 transformers (up to 345kV) developed its own health index to rank its fleet. This index is based on periodic oil sampling of main tank and OLTC tank and bushing testing. No data from online monitoring systems were available or used in the index.

Individual component scores were on a scale of 1-3 with 1 = Good, 2 = Degraded, and 3 = Unacceptable. Sometimes the scores included a four which equated to End of Life.

Users can follow the example matrix developed by the Working Group (after defining the time scales) or they may also develop their own matrix with any number of levels. It is, however, essential that there is a clear definition of each level or category to ensure that scores for each failure mode being assessed can be applied consistently.

This utility developed its own scoring methodology using the change or increase in the total index number from one year to the next. The difference (higher number) was the key method to determine the ranking. This evaluates the condition of two 42-year-old transformers from the same OEM and same vintage.

Taking their scoring method and using the overall component scoring method described by CIGRETB 761, translated into colors (worst case approach) together with the summation method produced the charts in Figure 2 and Figure 3.

Figure 2 – Transformer 2 condition assessment
Figure 3 – Transformer 3 condition assessment

The chart itself is another representation relating ‘worst case’ together with ‘summation score’ and provides a visual representation towards identifying candidates for replacement, refurbishment or maintenance. It can also be used to guide the asset owner toward one of these three options, based on their company policy.

In the case of unit T2, (Score 20 Red), the poor degree of polymerization (DP) results (inferred from furan in oil measurements), acceptable (not good or bad yet) dissipation factor results, and poor oil DGA and oil quality, all point towards a replacement option from a technical condition point of view.

In the case of unit T1, (Score 12 Pink), most indicators point towards maintenance issues but not yet at a replacement issue. It may be worthwhile to increase the maintenance and assess the loading imposed on the unit. The transformer loading guide (IEE C57.91 or IEC 60076-7) can be used to estimate the loss of life of paper due to its historical thermal load.

In this example, utility health assessment, one of the main variances from CIGRE TB 761 is the use of the absolute change (or deviation) of the total score from the prior year’s result.

The takeaway message is this: Identical units in terms of vintage, years in service, and in parallel operation, always exhibit different patterns of behavior, they must be treated as individuals.

TAI 3 – Transformer spares

Another utility developed an index to assess the condition of their fleet of spare transformers and to determine which ones are fit for service and ready for deployment. This index was also useful in helping to identify transformers that have reached their end of life and should be scrapped and those that require some repairs.

Table 6 – Scoring matrix for transformer spares

The sources of the information were from:

  • Visual assessments recording key information and photographs;
  • Electrical tests e.g. insulation resistance and dielectric dissipation factor (DDF) of any condenser bushings that were fitted in the transformer and had test taps;
  • Dissolved Gas Analysis (DGA) from a dielectric fluid sample from the main tank and OLTC taken at the time of visual assessment, but also reviewing any historic results available.

This assessment also evaluated the storage site or substation for bunding, transport access, and security.

There are many other considerations in making asset management decisions. CIGRE TB 248 “Economics on Transformer Management” describes a methodology that could be used in addition to the TAI, to arrive at a final decision.

Identify upfront what is repairable and then consider whether it is economical. Examples of repairable items are:

  • Bushing and OLTC maintenance and bushing replacement
  • Replacing desiccant in breathers
  • Tank repairs and repainting
  • Dielectric fluid – oil filtrations

After an assessment, there is a lot of information and the challenge is to combine that into a single number for ranking. If you only considered one aspect of information like only considering the degree of polymerization (shown in Table 7) or the assembly state (shown in Table 8), the index shows 54 and 59 transformers ready for deployment.

Table7 – Investigation on the DP (degree of polymerization)

Note: Five transformers were not included in the list above because they either had no sample point with a valve and/or were assumed to be empty of oil and stored under nitrogen.

Table 8 – Investigation on the assembly state of the spare transformers.

Users can use a combination of the scores/condition state for a complete assessment. In the case above, the user had found one spare transformer in an as-new condition but was missing bushings, and therefore it was not ready for use until the bushings were found.

A series of score reduction criteria were also applied to help determine the overall index to highlight deployment readiness. Some of the scoring reductions believed to be useful in this TAI are shown below;

  • If a transformer is fully assembled, it gets more points over units that have missing parts (refer to Table 8)
  • Core/frame ground was scored very harshly because that could be an indication of transport damage for these transformers, which is potentially very severe.
  • The score was reduced for transformers where the presence of potentially corrosive sulphur was detected.
  • There is no reduction for “new” insulation but a significant scoring reduction for insulation that is at a DP 100 – 200 when it has reached the end of life.

After completing an advanced condition assessment and combining all available sources of information, using a Hybrid Score (#5), it is possible to make the following observations.

In this example, the overall scores ranged between 40 and 97%; if a transformer had perfect responses to all questions, it would equal 100%.

If this TAI is sorted based on readiness to be deployed, the results are:

Figure4 –Transformer spares assessment index

The top 29 transformers were all built this century.

Note there was no discrimination/weighting on age or year of manufacture just physical condition.

Even some of the highest-ranked transformers have minor problems.

In the blue category shown above in Figure 5, some of those transformers had a bad test result regarding the presence of potentially corrosive sulphur resulting in their scores being reduced (by 5%). This is to reflect the increased risk of failure. Recently, numerous failures of transformers have been related to the formation of copper and silver sulphide on metal surfaces and copper sulphide deposits in the insulating paper in the windings (CIGRE TB 625). Oil could be passivated to correct, but unless these are going to be heavily loaded, the risks of using as-is are manageable. Therefore, it could be argued that the eight transformers in blue with just those issues could be moved straight to the green category, giving 27 transformers that are okay and ready to go, and 16 needing minor work.

In the significant issues category, also from Figure 5, 14 out of 27 are on this list for failing bushing testing. However, many of these bushings might pass with maintenance and retesting. As the worst case, the transformers can be fixed by replacing the bushings and can easily move up the list and not down the list. That is to say that this failure mode is repairable and not a recommendation for the transformer to be scrapped.

This example shows how a TAI can be helpful in determining the overall condition of a spare fleet and what repair work is needed on spare transformers before they go into service.


In developing an index, the transformer user or Asset Manager must have a clear understanding from the outset about the intended purpose of the index, as the purpose will determine how the index is constructed to ensure that the appropriate decisions are made. If a condition is detected indicating imminent failure, prompt action should be taken.


Peter Cole and members of Cigre Working Group A2.49


  1. Condition Assessment for Transformers & Components, Sparling, Beckett, MacArthur, TechCon AUS_NZ, 2019.
  2. CIGRE Technical Brochure 761 – Condition Assessment of Power Transformers, 2019
  3. CIGRE Technical Brochure 625 – Copper Sulphide Long Term Mitigation and Risk Assessment, 2015.
  4. CIGRE Technical Brochure 445 – Guide for Transformer Maintenance, 2011.
  5. CIGRE Technical Brochure 248 – Guide on Economics of Transformer Management, 2004
  6. IEEE C57-140-2017 – Guide for the Evaluation and Reconditioning of Liquid-Immersed Power Transformers
  7. IEEE Guide C57.152-2013, Guide for Diagnostic Field Testing of Fluid-Filled Power Transformers, Regulators, and Reactors
  8. IEEE C57.91-2011 – Guide for Loading Mineral-Oil-Immersed Transformers, 2011.
  9. IEC 60076-7 – Loading guide for mineral-oil-immersed power transformers, 2018.

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